Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations52413
Missing cells118346
Missing cells (%)14.1%
Duplicate rows1155
Duplicate rows (%)2.2%
Total size in memory8.8 MiB
Average record size in memory176.3 B

Variable types

Numeric14
Categorical2

Alerts

13170_FERM0101.DO_2_PV has constant value "0.0"Constant
Dataset has 1155 (2.2%) duplicate rowsDuplicates
13170_FERM0101.PUMP_1_PV is highly imbalanced (99.8%)Imbalance
13170_FERM0101.Agitation_PV has 4461 (8.5%) missing valuesMissing
13170_FERM0101.Air_Sparge_PV has 4461 (8.5%) missing valuesMissing
13170_FERM0101.Biocontainer_Pressure_PV has 4460 (8.5%) missing valuesMissing
13170_FERM0101.DO_1_PV has 4461 (8.5%) missing valuesMissing
13170_FERM0101.DO_2_PV has 50789 (96.9%) missing valuesMissing
13170_FERM0101.Gas_Overlay_PV has 4461 (8.5%) missing valuesMissing
13170_FERM0101.Load_Cell_Net_PV has 4460 (8.5%) missing valuesMissing
13170_FERM0101.pH_1_PV has 5108 (9.7%) missing valuesMissing
13170_FERM0101.pH_2_PV has 4460 (8.5%) missing valuesMissing
13170_FERM0101.PUMP_1_PV has 4461 (8.5%) missing valuesMissing
13170_FERM0101.PUMP_1_TOTAL has 4460 (8.5%) missing valuesMissing
13170_FERM0101.PUMP_2_PV has 4461 (8.5%) missing valuesMissing
13170_FERM0101.PUMP_2_TOTAL has 4460 (8.5%) missing valuesMissing
13170_FERM0101.Single_Use_DO_PV has 4461 (8.5%) missing valuesMissing
13170_FERM0101.Single_Use_pH_PV has 4461 (8.5%) missing valuesMissing
13170_FERM0101.Temperatura_PV has 4461 (8.5%) missing valuesMissing
13170_FERM0101.Agitation_PV has 22712 (43.3%) zerosZeros
13170_FERM0101.Air_Sparge_PV has 44491 (84.9%) zerosZeros
13170_FERM0101.DO_1_PV has 35465 (67.7%) zerosZeros
13170_FERM0101.Gas_Overlay_PV has 16552 (31.6%) zerosZeros
13170_FERM0101.Load_Cell_Net_PV has 952 (1.8%) zerosZeros
13170_FERM0101.PUMP_1_TOTAL has 2802 (5.3%) zerosZeros
13170_FERM0101.PUMP_2_PV has 41361 (78.9%) zerosZeros
13170_FERM0101.PUMP_2_TOTAL has 15718 (30.0%) zerosZeros

Reproduction

Analysis started2024-09-29 18:17:35.655965
Analysis finished2024-09-29 18:17:50.457570
Duration14.8 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

13170_FERM0101.Agitation_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct779
Distinct (%)1.6%
Missing4461
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean29.692431
Minimum0
Maximum84
Zeros22712
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:17:50.503535image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q380
95-th percentile80
Maximum84
Range84
Interquartile range (IQR)80

Descriptive statistics

Standard deviation34.858793
Coefficient of variation (CV)1.173996
Kurtosis-1.4213791
Mean29.692431
Median Absolute Deviation (MAD)20
Skewness0.62556787
Sum1423811.4
Variance1215.1355
MonotonicityNot monotonic
2024-09-29T20:17:50.571384image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22712
43.3%
80 13845
26.4%
20 8610
 
16.4%
36 645
 
1.2%
40 576
 
1.1%
84 218
 
0.4%
79.68499756 111
 
0.2%
79.67000122 93
 
0.2%
79.65499878 76
 
0.1%
44 65
 
0.1%
Other values (769) 1001
 
1.9%
(Missing) 4461
 
8.5%
ValueCountFrequency (%)
0 22712
43.3%
20 8610
 
16.4%
20.0020768 1
 
< 0.1%
20.06867879 1
 
< 0.1%
20.09342888 1
 
< 0.1%
20.47142792 1
 
< 0.1%
20.51386088 1
 
< 0.1%
20.55714264 1
 
< 0.1%
20.5946035 1
 
< 0.1%
20.65714264 1
 
< 0.1%
ValueCountFrequency (%)
84 218
 
0.4%
80 13845
26.4%
79.9992932 1
 
< 0.1%
79.89599753 1
 
< 0.1%
79.83249055 1
 
< 0.1%
79.81953964 1
 
< 0.1%
79.78946928 1
 
< 0.1%
79.7 20
 
< 0.1%
79.69697277 1
 
< 0.1%
79.69690842 1
 
< 0.1%

13170_FERM0101.Air_Sparge_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct3462
Distinct (%)7.2%
Missing4461
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean2.9004484
Minimum0
Maximum160.08291
Zeros44491
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:17:50.638873image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile23.695022
Maximum160.08291
Range160.08291
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.290201
Coefficient of variation (CV)4.2373452
Kurtosis20.335335
Mean2.9004484
Median Absolute Deviation (MAD)0
Skewness4.4892238
Sum139082.3
Variance151.04904
MonotonicityNot monotonic
2024-09-29T20:17:50.713934image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44491
84.9%
64.15761628 1
 
< 0.1%
23.072867 1
 
< 0.1%
19.10034437 1
 
< 0.1%
29.22128906 1
 
< 0.1%
43.8859314 1
 
< 0.1%
1.803031983 1
 
< 0.1%
62.91697998 1
 
< 0.1%
13.97447851 1
 
< 0.1%
35.00180359 1
 
< 0.1%
Other values (3452) 3452
 
6.6%
(Missing) 4461
 
8.5%
ValueCountFrequency (%)
0 44491
84.9%
0.002593287407 1
 
< 0.1%
0.0399603269 1
 
< 0.1%
0.06761282211 1
 
< 0.1%
0.06829175277 1
 
< 0.1%
0.1256828547 1
 
< 0.1%
0.204037714 1
 
< 0.1%
0.260233666 1
 
< 0.1%
0.2726849476 1
 
< 0.1%
0.4026365064 1
 
< 0.1%
ValueCountFrequency (%)
160.0829064 1
< 0.1%
160.0290024 1
< 0.1%
160.0273496 1
< 0.1%
160.0053029 1
< 0.1%
159.9894573 1
< 0.1%
159.9856798 1
< 0.1%
65.14790723 1
< 0.1%
65.1366272 1
< 0.1%
65.07252359 1
< 0.1%
64.9895874 1
< 0.1%

13170_FERM0101.Biocontainer_Pressure_PV
Real number (ℝ)

MISSING 

Distinct23622
Distinct (%)49.3%
Missing4460
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean139.79091
Minimum-5.8685431
Maximum480
Zeros0
Zeros (%)0.0%
Negative23415
Negative (%)44.7%
Memory size2.8 MiB
2024-09-29T20:17:50.787463image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-5.8685431
5-th percentile-2.4183727
Q1-1.1364008
median0.10706177
Q3480
95-th percentile480
Maximum480
Range485.86854
Interquartile range (IQR)481.1364

Descriptive statistics

Standard deviation218.45633
Coefficient of variation (CV)1.5627363
Kurtosis-1.16231
Mean139.79091
Median Absolute Deviation (MAD)1.5067611
Skewness0.9151489
Sum6703393.4
Variance47723.169
MonotonicityNot monotonic
2024-09-29T20:17:50.862853image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
480 13999
26.7%
0.1678222656 244
 
0.5%
-0.6828674316 223
 
0.4%
-0.703125 220
 
0.4%
-1.3715271 219
 
0.4%
-0.9259277344 208
 
0.4%
-1.168981934 205
 
0.4%
0.188079834 201
 
0.4%
0.1475708008 198
 
0.4%
-1.148724365 196
 
0.4%
Other values (23612) 32040
61.1%
(Missing) 4460
 
8.5%
ValueCountFrequency (%)
-5.868543094 1
< 0.1%
-5.629517208 1
< 0.1%
-5.240161133 1
< 0.1%
-5.1996521 1
< 0.1%
-5.187527926 1
< 0.1%
-5.086044468 1
< 0.1%
-5.085721388 1
< 0.1%
-5.057867432 1
< 0.1%
-5.041116826 1
< 0.1%
-5.037615967 1
< 0.1%
ValueCountFrequency (%)
480 13999
26.7%
66.94743554 1
 
< 0.1%
51.83739014 1
 
< 0.1%
14.63064017 1
 
< 0.1%
10.93886667 1
 
< 0.1%
10.75693679 1
 
< 0.1%
10.74510228 1
 
< 0.1%
10.74074097 2
 
< 0.1%
10.72358127 1
 
< 0.1%
10.69196393 1
 
< 0.1%

13170_FERM0101.DO_1_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct7705
Distinct (%)16.1%
Missing4461
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean7.2651856
Minimum0
Maximum137.40568
Zeros35465
Zeros (%)67.7%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:17:51.656977image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39.1240956
95-th percentile45.5831
Maximum137.40568
Range137.40568
Interquartile range (IQR)9.1240956

Descriptive statistics

Standard deviation16.070681
Coefficient of variation (CV)2.2120124
Kurtosis9.4598268
Mean7.2651856
Median Absolute Deviation (MAD)0
Skewness2.946763
Sum348380.18
Variance258.26677
MonotonicityNot monotonic
2024-09-29T20:17:51.729312image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35465
67.7%
55.85585327 103
 
0.2%
56.00424194 94
 
0.2%
70.66193848 52
 
0.1%
70.52768555 47
 
0.1%
55.2729187 47
 
0.1%
55.41600342 40
 
0.1%
70.11995239 40
 
0.1%
55.42659912 35
 
0.1%
69.15532227 32
 
0.1%
Other values (7695) 11997
 
22.9%
(Missing) 4461
 
8.5%
ValueCountFrequency (%)
0 35465
67.7%
1.97652874 1
 
< 0.1%
2.545617294 5
 
< 0.1%
2.574977493 1
 
< 0.1%
2.68391037 1
 
< 0.1%
2.698154259 2
 
< 0.1%
2.802563858 1
 
< 0.1%
2.827325439 1
 
< 0.1%
2.844734001 1
 
< 0.1%
2.878373146 1
 
< 0.1%
ValueCountFrequency (%)
137.4056763 6
< 0.1%
137.3957397 1
 
< 0.1%
134.7804304 1
 
< 0.1%
133.4128784 1
 
< 0.1%
130.2554443 1
 
< 0.1%
127.9234253 2
 
< 0.1%
125.5814575 1
 
< 0.1%
124.4825562 1
 
< 0.1%
120.9124146 1
 
< 0.1%
119.9527588 1
 
< 0.1%

13170_FERM0101.DO_2_PV
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing50789
Missing (%)96.9%
Memory size2.8 MiB
0.0
1624 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4872
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1624
 
3.1%
(Missing) 50789
96.9%

Length

2024-09-29T20:17:51.798666image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T20:17:51.849809image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1624
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3248
66.7%
. 1624
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4872
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3248
66.7%
. 1624
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4872
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3248
66.7%
. 1624
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4872
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3248
66.7%
. 1624
33.3%

13170_FERM0101.Gas_Overlay_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct31400
Distinct (%)65.5%
Missing4461
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean2.6737002
Minimum0
Maximum18.399838
Zeros16552
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:17:51.905640image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.9995634
Q34.0001681
95-th percentile4.0010143
Maximum18.399838
Range18.399838
Interquartile range (IQR)4.0001681

Descriptive statistics

Standard deviation2.1302499
Coefficient of variation (CV)0.79674224
Kurtosis5.8287947
Mean2.6737002
Median Absolute Deviation (MAD)0.00094988447
Skewness0.73766625
Sum128209.27
Variance4.5379646
MonotonicityNot monotonic
2024-09-29T20:17:51.979071image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16552
31.6%
4.000392648 2
 
< 0.1%
3.999281896 1
 
< 0.1%
4.000703796 1
 
< 0.1%
3.999554202 1
 
< 0.1%
3.999433701 1
 
< 0.1%
4.000941869 1
 
< 0.1%
3.999970251 1
 
< 0.1%
4.001452479 1
 
< 0.1%
3.999994238 1
 
< 0.1%
Other values (31390) 31390
59.9%
(Missing) 4461
 
8.5%
ValueCountFrequency (%)
0 16552
31.6%
1.590095181 1
 
< 0.1%
1.594926728 1
 
< 0.1%
1.595387055 1
 
< 0.1%
1.596584886 1
 
< 0.1%
1.597893892 1
 
< 0.1%
1.598017001 1
 
< 0.1%
1.598178277 1
 
< 0.1%
1.598417897 1
 
< 0.1%
1.598425739 1
 
< 0.1%
ValueCountFrequency (%)
18.39983836 1
< 0.1%
18.30288376 1
< 0.1%
16.02017071 1
< 0.1%
16.01059109 1
< 0.1%
16.00595521 1
< 0.1%
16.00560157 1
< 0.1%
16.00550007 1
< 0.1%
16.00530343 1
< 0.1%
16.00504215 1
< 0.1%
16.00489154 1
< 0.1%

13170_FERM0101.Load_Cell_Net_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct2182
Distinct (%)4.6%
Missing4460
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean829.76313
Minimum-84.4
Maximum1732.4
Zeros952
Zeros (%)1.8%
Negative17269
Negative (%)32.9%
Memory size2.8 MiB
2024-09-29T20:17:52.052703image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-84.4
5-th percentile-19.2
Q1-17.6
median1511.2
Q31596.4
95-th percentile1662.8
Maximum1732.4
Range1816.8
Interquartile range (IQR)1614

Descriptive statistics

Standard deviation801.45902
Coefficient of variation (CV)0.96588893
Kurtosis-1.9715843
Mean829.76313
Median Absolute Deviation (MAD)166.8
Skewness-0.077566454
Sum39789631
Variance642336.57
MonotonicityNot monotonic
2024-09-29T20:17:52.127037image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18.8 2718
 
5.2%
-18 2672
 
5.1%
-17.2 2610
 
5.0%
-19.2 1670
 
3.2%
-18.4 1586
 
3.0%
-17.6 1162
 
2.2%
-19.6 991
 
1.9%
0 952
 
1.8%
0.4 741
 
1.4%
1598.8 711
 
1.4%
Other values (2172) 32140
61.3%
(Missing) 4460
 
8.5%
ValueCountFrequency (%)
-84.4 91
0.2%
-84.08163405 1
 
< 0.1%
-84.07962254 1
 
< 0.1%
-84 36
 
0.1%
-70.4 1
 
< 0.1%
-70 1
 
< 0.1%
-69.77818471 1
 
< 0.1%
-69.6 1
 
< 0.1%
-68 17
 
< 0.1%
-67.6 78
0.1%
ValueCountFrequency (%)
1732.4 3
 
< 0.1%
1732 1
 
< 0.1%
1706.24 1
 
< 0.1%
1693.85867 1
 
< 0.1%
1680.8 4
 
< 0.1%
1680.4 15
< 0.1%
1680 8
 
< 0.1%
1679.6 5
 
< 0.1%
1679.2 5
 
< 0.1%
1678.8 20
< 0.1%

13170_FERM0101.pH_1_PV
Real number (ℝ)

MISSING 

Distinct6612
Distinct (%)14.0%
Missing5108
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean3.8343682
Minimum-1.4993618
Maximum11.540951
Zeros14
Zeros (%)< 0.1%
Negative11
Negative (%)< 0.1%
Memory size2.8 MiB
2024-09-29T20:17:52.200764image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-1.4993618
5-th percentile1.3058979
Q11.5838192
median3.8540385
Q35.8773048
95-th percentile6.0398643
Maximum11.540951
Range13.040313
Interquartile range (IQR)4.2934856

Descriptive statistics

Standard deviation2.0880752
Coefficient of variation (CV)0.54456824
Kurtosis-1.8102927
Mean3.8343682
Median Absolute Deviation (MAD)2.0825629
Skewness-0.077898074
Sum181384.79
Variance4.3600579
MonotonicityNot monotonic
2024-09-29T20:17:52.279337image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.305897903 3115
 
5.9%
1.572478104 2033
 
3.9%
1.605526733 1228
 
2.3%
1.662327576 1071
 
2.0%
1.421878052 920
 
1.8%
1.627259445 734
 
1.4%
1.598772621 641
 
1.2%
3.585752487 637
 
1.2%
1.550415421 608
 
1.2%
3.515829086 481
 
0.9%
Other values (6602) 35837
68.4%
(Missing) 5108
 
9.7%
ValueCountFrequency (%)
-1.499361801 1
 
< 0.1%
-0.2921211243 10
 
< 0.1%
0 14
 
< 0.1%
0.165086937 1
 
< 0.1%
0.2312469482 1
 
< 0.1%
1.305897903 3115
5.9%
1.357866669 63
 
0.1%
1.365427143 1
 
< 0.1%
1.367677061 1
 
< 0.1%
1.370015639 1
 
< 0.1%
ValueCountFrequency (%)
11.54095078 1
 
< 0.1%
11.20732269 1
 
< 0.1%
10.95962372 3
 
< 0.1%
10.61732682 1
 
< 0.1%
10.40258408 2
 
< 0.1%
10.39228887 1
 
< 0.1%
10.37986679 17
< 0.1%
10.35630875 1
 
< 0.1%
9.779455566 4
 
< 0.1%
9.697975159 1
 
< 0.1%

13170_FERM0101.pH_2_PV
Real number (ℝ)

MISSING 

Distinct197
Distinct (%)0.4%
Missing4460
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean-23.006696
Minimum-38.210712
Maximum178.7813
Zeros26
Zeros (%)< 0.1%
Negative47253
Negative (%)90.2%
Memory size2.8 MiB
2024-09-29T20:17:52.351607image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-38.210712
5-th percentile-38.210712
Q1-38.210712
median-38.210712
Q3-0.13858643
95-th percentile-0.13858643
Maximum178.7813
Range216.99201
Interquartile range (IQR)38.072125

Descriptive statistics

Standard deviation18.846901
Coefficient of variation (CV)-0.81919199
Kurtosis-0.051468657
Mean-23.006696
Median Absolute Deviation (MAD)0
Skewness0.5791745
Sum-1103240.1
Variance355.20567
MonotonicityNot monotonic
2024-09-29T20:17:52.418979image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-38.21071167 28873
55.1%
-0.1385864258 16902
32.2%
-0.1648593903 1388
 
2.6%
3.329538727 369
 
0.7%
5.159653854 39
 
0.1%
0 26
 
< 0.1%
5.150754166 24
 
< 0.1%
5.186676025 22
 
< 0.1%
3.198595047 19
 
< 0.1%
5.19525795 19
 
< 0.1%
Other values (187) 272
 
0.5%
(Missing) 4460
 
8.5%
ValueCountFrequency (%)
-38.21071167 28873
55.1%
-32.91919982 1
 
< 0.1%
-31.54859559 1
 
< 0.1%
-30.86112923 1
 
< 0.1%
-30.80153819 1
 
< 0.1%
-30.64641976 1
 
< 0.1%
-30.61765785 1
 
< 0.1%
-30.50369281 1
 
< 0.1%
-30.49559705 1
 
< 0.1%
-30.45100966 1
 
< 0.1%
ValueCountFrequency (%)
178.7812988 4
< 0.1%
178.1812988 1
 
< 0.1%
175.8241699 1
 
< 0.1%
158.660083 1
 
< 0.1%
11.98619843 1
 
< 0.1%
5.528670502 1
 
< 0.1%
5.515440458 1
 
< 0.1%
5.502289581 1
 
< 0.1%
5.500537002 1
 
< 0.1%
5.475909042 1
 
< 0.1%

13170_FERM0101.PUMP_1_PV
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing4461
Missing (%)8.5%
Memory size2.8 MiB
0.0
47940 
48.0
 
10
37.6878392534044
 
1
10.06969766905144
 
1

Length

Max length17
Median length3
Mean length3.0007716
Min length3

Characters and Unicode

Total characters143893
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 47940
91.5%
48.0 10
 
< 0.1%
37.6878392534044 1
 
< 0.1%
10.06969766905144 1
 
< 0.1%
(Missing) 4461
 
8.5%

Length

2024-09-29T20:17:52.488232image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T20:17:52.542353image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 47940
> 99.9%
48.0 10
 
< 0.1%
37.6878392534044 1
 
< 0.1%
10.06969766905144 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 95894
66.6%
. 47952
33.3%
4 15
 
< 0.1%
8 12
 
< 0.1%
6 5
 
< 0.1%
9 4
 
< 0.1%
3 3
 
< 0.1%
7 3
 
< 0.1%
5 2
 
< 0.1%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 143893
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 95894
66.6%
. 47952
33.3%
4 15
 
< 0.1%
8 12
 
< 0.1%
6 5
 
< 0.1%
9 4
 
< 0.1%
3 3
 
< 0.1%
7 3
 
< 0.1%
5 2
 
< 0.1%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 143893
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 95894
66.6%
. 47952
33.3%
4 15
 
< 0.1%
8 12
 
< 0.1%
6 5
 
< 0.1%
9 4
 
< 0.1%
3 3
 
< 0.1%
7 3
 
< 0.1%
5 2
 
< 0.1%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 143893
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 95894
66.6%
. 47952
33.3%
4 15
 
< 0.1%
8 12
 
< 0.1%
6 5
 
< 0.1%
9 4
 
< 0.1%
3 3
 
< 0.1%
7 3
 
< 0.1%
5 2
 
< 0.1%
1 2
 
< 0.1%

13170_FERM0101.PUMP_1_TOTAL
Real number (ℝ)

MISSING  ZEROS 

Distinct174
Distinct (%)0.4%
Missing4460
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean54.089623
Minimum0
Maximum8199.6
Zeros2802
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:17:52.604671image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.6
median18
Q334.8
95-th percentile58.8
Maximum8199.6
Range8199.6
Interquartile range (IQR)25.2

Descriptive statistics

Standard deviation227.28527
Coefficient of variation (CV)4.2020125
Kurtosis159.66017
Mean54.089623
Median Absolute Deviation (MAD)10.8
Skewness9.2183072
Sum2593759.7
Variance51658.596
MonotonicityNot monotonic
2024-09-29T20:17:52.672729image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.8 4108
 
7.8%
10.8 3615
 
6.9%
0 2802
 
5.3%
13.2 2650
 
5.1%
9.6 2632
 
5.0%
7.2 2309
 
4.4%
34.8 2241
 
4.3%
12 1863
 
3.6%
20.4 1856
 
3.5%
3.6 1844
 
3.5%
Other values (164) 22033
42.0%
(Missing) 4460
 
8.5%
ValueCountFrequency (%)
0 2802
5.3%
0.04389663605 1
 
< 0.1%
0.06540049546 1
 
< 0.1%
0.07728355838 1
 
< 0.1%
0.08697091041 1
 
< 0.1%
0.105630452 1
 
< 0.1%
0.119300106 1
 
< 0.1%
0.121942225 1
 
< 0.1%
0.1232024746 1
 
< 0.1%
0.158445678 1
 
< 0.1%
ValueCountFrequency (%)
8199.6 2
 
< 0.1%
7966.627657 1
 
< 0.1%
6886.6272 1
 
< 0.1%
5806.628343 1
 
< 0.1%
4726.6272 1
 
< 0.1%
3646.628057 1
 
< 0.1%
2566.6272 1
 
< 0.1%
1486.6272 1
 
< 0.1%
1458 1062
2.0%
862.8282935 1
 
< 0.1%

13170_FERM0101.PUMP_2_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct5905
Distinct (%)12.3%
Missing4461
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean0.53325464
Minimum0
Maximum48
Zeros41361
Zeros (%)78.9%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:17:52.741608image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.9203722
Maximum48
Range48
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7312239
Coefficient of variation (CV)3.2465238
Kurtosis67.420664
Mean0.53325464
Median Absolute Deviation (MAD)0
Skewness5.1352058
Sum25570.626
Variance2.9971362
MonotonicityNot monotonic
2024-09-29T20:17:52.812300image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41361
78.9%
8 540
 
1.0%
7.609883881 17
 
< 0.1%
7.610464478 16
 
< 0.1%
2.400146484 15
 
< 0.1%
7.200439453 14
 
< 0.1%
2.399963379 13
 
< 0.1%
7.199890137 10
 
< 0.1%
4.799926758 10
 
< 0.1%
4.800292969 9
 
< 0.1%
Other values (5895) 5947
 
11.3%
(Missing) 4461
 
8.5%
ValueCountFrequency (%)
0 41361
78.9%
8.509316472 × 10-51
 
< 0.1%
0.0005753878656 1
 
< 0.1%
0.001432784753 1
 
< 0.1%
0.001760184158 1
 
< 0.1%
0.002053044662 1
 
< 0.1%
0.0020584018 1
 
< 0.1%
0.002790056095 1
 
< 0.1%
0.002822782816 1
 
< 0.1%
0.003025307119 1
 
< 0.1%
ValueCountFrequency (%)
48 5
 
< 0.1%
8 540
1.0%
7.999996151 1
 
< 0.1%
7.999979688 1
 
< 0.1%
7.999961993 1
 
< 0.1%
7.999867251 1
 
< 0.1%
7.999805999 1
 
< 0.1%
7.999661967 1
 
< 0.1%
7.999554589 1
 
< 0.1%
7.999327633 1
 
< 0.1%

13170_FERM0101.PUMP_2_TOTAL
Real number (ℝ)

MISSING  ZEROS 

Distinct8676
Distinct (%)18.1%
Missing4460
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean4111.4518
Minimum0
Maximum21070.669
Zeros15718
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:17:52.882243image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2734.1549
Q37083.582
95-th percentile11393.57
Maximum21070.669
Range21070.669
Interquartile range (IQR)7083.582

Descriptive statistics

Standard deviation4528.4824
Coefficient of variation (CV)1.1014315
Kurtosis1.649706
Mean4111.4518
Median Absolute Deviation (MAD)2734.1549
Skewness1.147215
Sum1.9715645 × 108
Variance20507153
MonotonicityNot monotonic
2024-09-29T20:17:52.951370image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15718
30.0%
11393.56953 4070
 
7.8%
7083.582031 2198
 
4.2%
7952.075 1332
 
2.5%
5785.837891 1299
 
2.5%
7154.80625 1254
 
2.4%
21070.66875 852
 
1.6%
7487.278906 843
 
1.6%
7805.588281 759
 
1.4%
243.3584717 711
 
1.4%
Other values (8666) 18917
36.1%
(Missing) 4460
 
8.5%
ValueCountFrequency (%)
0 15718
30.0%
0.03316664088 1
 
< 0.1%
0.7841715813 1
 
< 0.1%
3.959999847 466
 
0.9%
5.601608658 1
 
< 0.1%
5.755989075 1
 
< 0.1%
9.019998169 1
 
< 0.1%
10.30824051 1
 
< 0.1%
12.2329375 1
 
< 0.1%
15.32780457 1
 
< 0.1%
ValueCountFrequency (%)
21070.66875 852
1.6%
21016.14219 1
 
< 0.1%
20983.20625 1
 
< 0.1%
20973.09375 1
 
< 0.1%
20924.70156 1
 
< 0.1%
20905.75313 1
 
< 0.1%
20843.77813 1
 
< 0.1%
20823.84063 1
 
< 0.1%
20806.07188 1
 
< 0.1%
20760.79844 1
 
< 0.1%

13170_FERM0101.Single_Use_DO_PV
Real number (ℝ)

MISSING 

Distinct10524
Distinct (%)21.9%
Missing4461
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean586.63223
Minimum0
Maximum905.0707
Zeros27
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:17:53.023159image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.454408
Q1663.8938
median715.94063
Q3799.99199
95-th percentile799.99199
Maximum905.0707
Range905.0707
Interquartile range (IQR)136.09819

Descriptive statistics

Standard deviation308.61539
Coefficient of variation (CV)0.52607984
Kurtosis-0.4365981
Mean586.63223
Median Absolute Deviation (MAD)84.051367
Skewness-1.1871057
Sum28130189
Variance95243.46
MonotonicityNot monotonic
2024-09-29T20:17:53.093271image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
799.9919922 18339
35.0%
703.9084473 4072
 
7.8%
715.940625 2031
 
3.9%
667.1603516 1934
 
3.7%
691.7952637 1233
 
2.4%
663.8937988 869
 
1.7%
766.2758301 793
 
1.5%
719.4172852 769
 
1.5%
709.4014648 670
 
1.3%
905.0707031 607
 
1.2%
Other values (10514) 16635
31.7%
(Missing) 4461
 
8.5%
ValueCountFrequency (%)
0 27
0.1%
0.9407118821 1
 
< 0.1%
0.9930820276 1
 
< 0.1%
1.008743496 1
 
< 0.1%
1.012209416 1
 
< 0.1%
1.092278305 1
 
< 0.1%
1.176531131 1
 
< 0.1%
1.188285434 1
 
< 0.1%
1.195094785 1
 
< 0.1%
1.217081385 1
 
< 0.1%
ValueCountFrequency (%)
905.0707031 607
 
1.2%
898.8673828 95
 
0.2%
883.7032722 1
 
< 0.1%
869.6510742 31
 
0.1%
799.9919922 18339
35.0%
797.8545018 1
 
< 0.1%
786.2329102 17
 
< 0.1%
784.1854526 1
 
< 0.1%
770.4497362 1
 
< 0.1%
766.2758301 793
 
1.5%

13170_FERM0101.Single_Use_pH_PV
Real number (ℝ)

MISSING 

Distinct2032
Distinct (%)4.2%
Missing4461
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean623.76427
Minimum-0.4
Maximum805.76797
Zeros0
Zeros (%)0.0%
Negative21
Negative (%)< 0.1%
Memory size2.8 MiB
2024-09-29T20:17:53.163692image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-0.4
5-th percentile5.8
Q1799.68799
median799.87197
Q3799.97598
95-th percentile800.048
Maximum805.76797
Range806.16797
Interquartile range (IQR)0.28798828

Descriptive statistics

Standard deviation330.01841
Coefficient of variation (CV)0.52907552
Kurtosis-0.20757163
Mean623.76427
Median Absolute Deviation (MAD)0.15200195
Skewness-1.3388099
Sum29910744
Variance108912.15
MonotonicityNot monotonic
2024-09-29T20:17:53.242806image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800.0239746 4454
 
8.5%
799.8959961 3982
 
7.6%
799.8160156 3136
 
6.0%
800.0319824 2215
 
4.2%
799.8560059 1817
 
3.5%
799.7199707 1807
 
3.4%
800.0399902 1585
 
3.0%
799.9120117 1410
 
2.7%
799.8399902 1349
 
2.6%
799.952002 1338
 
2.6%
Other values (2022) 24859
47.4%
(Missing) 4461
 
8.5%
ValueCountFrequency (%)
-0.4 1
 
< 0.1%
-0.1919998169 1
 
< 0.1%
-0.1759998322 3
< 0.1%
-0.1520000458 1
 
< 0.1%
-0.1439998627 1
 
< 0.1%
-0.136000061 3
< 0.1%
-0.1040000916 1
 
< 0.1%
-0.09599990845 1
 
< 0.1%
-0.07999992371 4
< 0.1%
-0.07200012207 2
< 0.1%
ValueCountFrequency (%)
805.7679688 469
 
0.9%
800.1359863 266
 
0.5%
800.1040039 617
 
1.2%
800.0719727 461
 
0.9%
800.047998 1114
 
2.1%
800.0399902 1585
 
3.0%
800.0319824 2215
4.2%
800.0239746 4454
8.5%
800 362
 
0.7%
799.9919922 364
 
0.7%

13170_FERM0101.Temperatura_PV
Real number (ℝ)

MISSING 

Distinct23846
Distinct (%)49.7%
Missing4461
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean17.41517
Minimum-0.4
Maximum81.073239
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size2.8 MiB
2024-09-29T20:17:53.318734image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-0.4
5-th percentile3.1840027
Q112.856006
median16.112
Q329.551464
95-th percentile29.631995
Maximum81.073239
Range81.473239
Interquartile range (IQR)16.695458

Descriptive statistics

Standard deviation9.1533596
Coefficient of variation (CV)0.52559691
Kurtosis-1.0092319
Mean17.41517
Median Absolute Deviation (MAD)7.2079956
Skewness-0.027527222
Sum835092.24
Variance83.783993
MonotonicityNot monotonic
2024-09-29T20:17:53.395434image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.6 2139
 
4.1%
29.60799561 1900
 
3.6%
29.57600098 1339
 
2.6%
3.208001709 1244
 
2.4%
29.63199463 1142
 
2.2%
3.184002686 987
 
1.9%
3.223999023 838
 
1.6%
29.55999756 553
 
1.1%
3.176000977 508
 
1.0%
29.64000244 432
 
0.8%
Other values (23836) 36870
70.3%
(Missing) 4461
 
8.5%
ValueCountFrequency (%)
-0.4 1
 
< 0.1%
3.023999023 1
 
< 0.1%
3.047998047 3
 
< 0.1%
3.064001465 1
 
< 0.1%
3.064208646 1
 
< 0.1%
3.073570447 1
 
< 0.1%
3.073635563 1
 
< 0.1%
3.079998779 8
< 0.1%
3.096002197 8
< 0.1%
3.103990935 1
 
< 0.1%
ValueCountFrequency (%)
81.07323879 1
< 0.1%
80.56978423 1
< 0.1%
32.43199463 1
< 0.1%
32.42562641 1
< 0.1%
32.41154145 1
< 0.1%
32.34565196 1
< 0.1%
32.34399414 1
< 0.1%
32.32800293 1
< 0.1%
32.20336209 1
< 0.1%
32.09599609 1
< 0.1%

Interactions

2024-09-29T20:17:48.947788image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:36.040529image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:36.994659image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:37.975873image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:38.922627image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:39.874599image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:40.853963image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:41.820805image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:43.350851image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:44.245648image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:45.174205image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:46.097003image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:47.025286image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:47.948430image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:49.013444image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:36.129231image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:37.060067image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:38.040787image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:38.985623image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:39.940585image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:40.920252image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:41.884355image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:17:44.308890image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:17:47.087991image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:48.013559image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:17:42.674631image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:43.607703image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:44.510600image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:45.441331image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:46.367293image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:47.293155image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:48.234246image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:49.310606image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:36.410983image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:37.346560image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:38.320112image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:39.264646image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:40.239016image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:41.204460image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:17:37.419286image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:17:42.815628image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:43.741548image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:44.646805image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:45.577809image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:46.504432image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:47.429959image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:48.381157image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:49.454540image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:36.545421image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:37.495931image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:38.458935image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:39.404616image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:40.380234image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:41.344567image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:42.882210image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:43.806600image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:44.715422image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:45.646063image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:46.572614image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:47.497200image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:48.452236image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:49.519475image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:36.606022image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:37.561031image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:38.520244image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:39.468076image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:40.443805image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:41.408196image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:42.943002image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:43.863901image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:44.784908image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:45.707778image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:46.631841image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:47.558435image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:48.516563image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:49.585428image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:36.668430image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:37.626992image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:38.585242image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:39.532529image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:40.509143image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:41.475302image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:43.007299image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:43.924479image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:44.844933image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:45.769544image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:46.694787image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:47.621204image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:48.582710image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:49.652670image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:36.731636image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:37.694952image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:38.651310image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:39.599312image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:40.575575image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:41.541014image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:43.072670image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:43.986573image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:44.909158image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:45.833381image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:46.761456image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:47.685046image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:48.652390image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:49.719911image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:36.794308image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:37.761056image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:17:39.664563image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:17:41.607766image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:43.141405image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:44.045658image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:44.972582image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:45.893885image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:17:47.748761image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:17:40.708024image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:17:46.885695image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:47.808943image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:48.787433image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:49.861075image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:36.925254image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:37.901458image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:38.850786image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:39.801598image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:40.780799image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:41.747229image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:43.278602image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:44.174789image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:45.104943image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:46.027096image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:46.955547image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:47.877685image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:17:48.873256image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Missing values

2024-09-29T20:17:49.943034image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-29T20:17:50.094112image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-29T20:17:50.289515image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

13170_FERM0101.Agitation_PV13170_FERM0101.Air_Sparge_PV13170_FERM0101.Biocontainer_Pressure_PV13170_FERM0101.DO_1_PV13170_FERM0101.DO_2_PV13170_FERM0101.Gas_Overlay_PV13170_FERM0101.Load_Cell_Net_PV13170_FERM0101.pH_1_PV13170_FERM0101.pH_2_PV13170_FERM0101.PUMP_1_PV13170_FERM0101.PUMP_1_TOTAL13170_FERM0101.PUMP_2_PV13170_FERM0101.PUMP_2_TOTAL13170_FERM0101.Single_Use_DO_PV13170_FERM0101.Single_Use_pH_PV13170_FERM0101.Temperatura_PV
DateTime
2023-03-15 00:00:00.00080.00.00.2690980.0NaN3.9992821563.65.905050-0.1385860.06.00.0149.229419799.991992799.8399929.600000
2023-03-15 00:15:00.00080.00.0-0.0549850.0NaN4.0006691563.65.905050-0.1385860.06.00.0149.229419799.991992799.8399929.559998
2023-03-15 00:30:00.00080.00.00.1880800.0NaN4.0007641563.65.905050-0.1385860.06.00.0149.229419799.991992799.8399929.600000
2023-03-15 00:45:00.00080.00.0-0.0751770.0NaN3.9996471563.65.905050-0.1385860.06.00.0149.229419799.991992799.8399929.599935
2023-03-15 01:00:00.00080.00.0-0.0144650.0NaN3.9993291563.65.905050-0.1385860.06.00.0149.229419799.991992799.8399929.576001
2023-03-15 01:15:00.00080.00.0-0.0541500.0NaN3.9990831563.65.905050-0.1385860.06.00.0149.229419799.991992799.8399929.560331
2023-03-15 01:30:00.00080.00.00.0260440.0NaN4.0003561563.65.905202-0.1385860.06.00.0149.229419799.991992799.8399929.576001
2023-03-15 01:45:00.00080.00.0-0.0139200.0NaN4.0003331563.65.913279-0.1385860.06.00.0149.229419799.991992799.8399929.607996
2023-03-15 02:00:00.00080.00.0-0.0151070.0NaN3.9997091563.65.913279-0.1385860.06.00.0149.229419799.991992799.8399929.600084
2023-03-15 02:15:00.00080.00.00.0253160.0NaN4.0001421563.65.913279-0.1385860.06.00.0149.229419799.991992799.8399929.607996
13170_FERM0101.Agitation_PV13170_FERM0101.Air_Sparge_PV13170_FERM0101.Biocontainer_Pressure_PV13170_FERM0101.DO_1_PV13170_FERM0101.DO_2_PV13170_FERM0101.Gas_Overlay_PV13170_FERM0101.Load_Cell_Net_PV13170_FERM0101.pH_1_PV13170_FERM0101.pH_2_PV13170_FERM0101.PUMP_1_PV13170_FERM0101.PUMP_1_TOTAL13170_FERM0101.PUMP_2_PV13170_FERM0101.PUMP_2_TOTAL13170_FERM0101.Single_Use_DO_PV13170_FERM0101.Single_Use_pH_PV13170_FERM0101.Temperatura_PV
DateTime
2024-09-10 21:45:00.00080.00.0000000.84437818.6468050.03.9994501663.65.878986-0.1648590.032.41.8216891433.87363318.4946235.93600029.605602
2024-09-10 22:00:00.00080.00.0000000.81240326.2477290.04.0000851663.65.878986-0.1648590.032.41.6558311474.31699226.9980625.93600029.607996
2024-09-10 22:15:00.00080.039.4915011.21255013.4399200.03.9997801663.65.878986-0.1648590.032.41.6113751508.08222714.4465725.93032129.593037
2024-09-10 22:30:00.00080.046.3811681.66666913.9500530.03.9997351663.65.878986-0.1648590.032.41.3950571542.33613315.5402795.91200029.607996
2024-09-10 22:45:00.00080.052.8925781.90972314.7812470.04.0019431663.65.878986-0.1648590.032.41.3670891576.05214817.3594225.93600029.600000
2024-09-10 23:00:00.00080.00.0000000.74161316.6732830.04.0004841663.65.878986-0.1648590.032.40.8676221612.66933616.0690995.93600029.607996
2024-09-10 23:15:00.00080.02.5213710.82834815.4722410.04.0010141663.65.878986-0.1648590.032.40.3960701649.85781214.7576235.93600029.600000
2024-09-10 23:30:00.00080.00.0000000.71616521.8263750.04.0016321663.65.878986-0.1648590.032.40.5490961685.93046921.6476535.94400029.600000
2024-09-10 23:45:00.00080.048.2778861.54163312.9836640.04.0006851664.05.878986-0.1648590.032.40.4642471723.68867214.2955865.94158129.600000
2024-09-11 00:00:00.00080.064.3138852.39799314.0852480.04.0030111664.05.878986-0.1648590.032.40.9718871756.80664116.8940585.94400029.602386

Duplicate rows

Most frequently occurring

13170_FERM0101.Agitation_PV13170_FERM0101.Air_Sparge_PV13170_FERM0101.Biocontainer_Pressure_PV13170_FERM0101.DO_1_PV13170_FERM0101.DO_2_PV13170_FERM0101.Gas_Overlay_PV13170_FERM0101.Load_Cell_Net_PV13170_FERM0101.pH_1_PV13170_FERM0101.pH_2_PV13170_FERM0101.PUMP_1_PV13170_FERM0101.PUMP_1_TOTAL13170_FERM0101.PUMP_2_PV13170_FERM0101.PUMP_2_TOTAL13170_FERM0101.Single_Use_DO_PV13170_FERM0101.Single_Use_pH_PV13170_FERM0101.Temperatura_PV# duplicates
1154NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4460
3460.00.0480.00.0NaN0.0-18.81.421878-0.1648590.040.80.011393.569531703.908447800.02397514.22399913
2670.00.0480.00.0NaN0.0-19.21.305898-38.2107120.040.80.011393.569531703.908447800.02397514.57600112
3620.00.0480.00.0NaN0.0-18.81.421878-0.1648590.040.80.011393.569531703.908447800.02397514.48000511
9320.00.0480.00.0NaN0.0-17.21.572478-38.2107120.034.80.07083.582031715.940625799.89599614.69599611
9410.00.0480.00.0NaN0.0-17.21.572478-38.2107120.034.80.07083.582031715.940625799.89599614.83199511
9800.00.0480.00.0NaN0.0-17.21.572478-38.2107120.034.80.07083.582031715.940625799.89599615.45600611
3130.00.0480.00.0NaN0.0-18.81.305898-38.2107120.040.80.011393.569531703.908447800.02397514.70400410
9460.00.0480.00.0NaN0.0-17.21.572478-38.2107120.034.80.07083.582031715.940625799.89599614.93599910
9670.00.0480.00.0NaN0.0-17.21.572478-38.2107120.034.80.07083.582031715.940625799.89599615.26400110